



'''simple docstring'''






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
     lowerCAmelCase:   int					=		argparse.ArgumentParser(
         description=(
             'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
             ' Distillation'
         )
     )
     parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
     parser.add_argument('--model_name', default='roberta-large', type=str)
     parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
     parser.add_argument('--vocab_transform', action='store_true')
     lowerCAmelCase:   Dict					=		parser.parse_args()

     if args.model_type == "roberta":
          lowerCAmelCase:   Any					=		RobertaForMaskedLM.from_pretrained(args.model_name)
          lowerCAmelCase:   List[Any]					=		'roberta'
     elif args.model_type == "gpt2":
          lowerCAmelCase:   List[str]					=		GPTaLMHeadModel.from_pretrained(args.model_name)
          lowerCAmelCase:   Dict					=		'transformer'

     lowerCAmelCase:   str					=		model.state_dict()
     lowerCAmelCase:   str					=		{}

     # Embeddings #
     if args.model_type == "gpt2":
          for param_name in ["wte.weight", "wpe.weight"]:
               lowerCAmelCase:   Tuple					=		state_dict[F"{prefix}.{param_name}"]
     else:
          for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
               lowerCAmelCase:   Optional[Any]					=		F"{prefix}.embeddings.{w}.weight"
               lowerCAmelCase:   List[Any]					=		state_dict[param_name]
          for w in ["weight", "bias"]:
               lowerCAmelCase:   Optional[Any]					=		F"{prefix}.embeddings.LayerNorm.{w}"
               lowerCAmelCase:   Optional[Any]					=		state_dict[param_name]

    # Transformer Blocks #
     lowerCAmelCase:   int					=		0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          if args.model_type == "gpt2":
               for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                    for w in ["weight", "bias"]:
                         lowerCAmelCase:   List[Any]					=		state_dict[
                             F"{prefix}.h.{teacher_idx}.{layer}.{w}"
                         ]
               lowerCAmelCase:   int					=		state_dict[F"{prefix}.h.{teacher_idx}.attn.bias"]
          else:
               for layer in [
                   "attention.self.query",
                   "attention.self.key",
                   "attention.self.value",
                   "attention.output.dense",
                   "attention.output.LayerNorm",
                   "intermediate.dense",
                   "output.dense",
                   "output.LayerNorm",
               ]:
                    for w in ["weight", "bias"]:
                         lowerCAmelCase:   Dict					=		state_dict[
                             F"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
                         ]
          std_idx += 1

     # Language Modeling Head ###s
     if args.model_type == "roberta":
          for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
               lowerCAmelCase:   Union[str, Any]					=		state_dict[F"{layer}"]
          if args.vocab_transform:
               for w in ["weight", "bias"]:
                    lowerCAmelCase:   List[Any]					=		state_dict[F"lm_head.dense.{w}"]
                    lowerCAmelCase:   Union[str, Any]					=		state_dict[F"lm_head.layer_norm.{w}"]
     elif args.model_type == "gpt2":
          for w in ["weight", "bias"]:
               lowerCAmelCase:   List[Any]					=		state_dict[F"{prefix}.ln_f.{w}"]
          lowerCAmelCase:   Dict					=		state_dict['lm_head.weight']

     print(F"N layers selected for distillation: {std_idx}")
     print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

     print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
     torch.save(compressed_sd, args.dump_checkpoint)




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )